Methods of Smart Alarming intend to detect as
soon as possible novelty or anomaly in Data Streams. A review
is proposed to highlight the key points of using them. In case
of univariate data, the more suitable method is not the same
as for stationary variable or non-stationary variable. Multivariate
data set are often dealt with using unsupervised learning based
methods, either with fac-tor analysis (mostly PCA) or clustering
algorithms. Each of these methods must be applied in a spe-cific
situation: prior knowledge of possible anomalies should be
needed or not, learning data set can be large sized or not,
and so on. Some examples are outlined. Discussion underlines
the importance of having a prior knowledge of variable behaviour,
and to consider the global flow chart, including eventually
a data preprocessing.